Theory and Background¶

HONEE solves the time-dependent Navier-Stokes equations of compressible gas dynamics in a static Eulerian three-dimensional frame using unstructured high-order finite/spectral element spatial discretizations and explicit or implicit high-order time-stepping (available in PETSc). Moreover, the Navier-Stokes example has been developed using PETSc, so that the pointwise physics (defined at quadrature points) is separated from the parallelization and meshing concerns.

The Navier-Stokes Equations¶

The mathematical formulation (from [SHJ91]) is given in what follows. The compressible Navier-Stokes equations in conservative form are

(1)¶\[ \begin{aligned} \frac{\partial \rho}{\partial t} + \nabla \cdot \bm{U} &= 0 \\ \frac{\partial \bm{U}}{\partial t} + \nabla \cdot \left( \frac{\bm{U} \otimes \bm{U}}{\rho} + P \bm{I}_3 -\bm\sigma \right) - \rho \bm{b} &= 0 \\ \frac{\partial E}{\partial t} + \nabla \cdot \left( \frac{(E + P)\bm{U}}{\rho} -\bm{u} \cdot \bm{\sigma} - k \nabla T \right) - \rho \bm{b} \cdot \bm{u} &= 0 \, , \\ \end{aligned} \]

where \(\bm{\sigma} = \mu(\nabla \bm{u} + (\nabla \bm{u})^T + \lambda (\nabla \cdot \bm{u})\bm{I}_3)\) is the Cauchy (symmetric) stress tensor, with \(\mu\) the dynamic viscosity coefficient, and \(\lambda = - 2/3\) the Stokes hypothesis constant. In equations (1), \(\rho\) represents the volume mass density, \(U\) the momentum density (defined as \(\bm{U}=\rho \bm{u}\), where \(\bm{u}\) is the vector velocity field), \(E\) the total energy density (defined as \(E = \rho e\), where \(e\) is the total energy including thermal and kinetic but not potential energy), \(\bm{I}_3\) represents the \(3 \times 3\) identity matrix, \(\bm{b}\) is a body force vector (e.g., gravity vector \(\bm{g}\)), \(k\) the thermal conductivity constant, \(T\) represents the temperature, and \(P\) the pressure, given by the following equation of state

(2)¶\[ P = \left( {c_p}/{c_v} -1\right) \left( E - {\bm{U}\cdot\bm{U}}/{(2 \rho)} \right) \, , \]

where \(c_p\) is the specific heat at constant pressure and \(c_v\) is the specific heat at constant volume (that define \(\gamma = c_p / c_v\), the specific heat ratio).

The system (1) can be rewritten in vector form

(3)¶\[ \frac{\partial \bm{q}}{\partial t} + \nabla \cdot \bm{F}(\bm{q}) -S(\bm{q}) = 0 \, , \]

for the state variables 5-dimensional vector

\[ \bm{q} = \begin{pmatrix} \rho \\ \bm{U} \equiv \rho \bm{ u }\\ E \equiv \rho e \end{pmatrix} \begin{array}{l} \leftarrow\textrm{ volume mass density}\\ \leftarrow\textrm{ momentum density}\\ \leftarrow\textrm{ energy density} \end{array} \]

where the flux and the source terms, respectively, are given by

(4)¶\[ \begin{aligned} \bm{F}(\bm{q}) &= \underbrace{\begin{pmatrix} \bm{U}\\ {(\bm{U} \otimes \bm{U})}/{\rho} + P \bm{I}_3 \\ {(E + P)\bm{U}}/{\rho} \end{pmatrix}}_{\bm F_{\text{adv}}} + \underbrace{\begin{pmatrix} 0 \\ - \bm{\sigma} \\ - \bm{u} \cdot \bm{\sigma} - k \nabla T \end{pmatrix}}_{\bm F_{\text{diff}}},\\ S(\bm{q}) &= \begin{pmatrix} 0\\ \rho \bm{b}\\ \rho \bm{b}\cdot \bm{u} \end{pmatrix}. \end{aligned} \]

Finite Element Formulation (Spatial Discretization)¶

Let the discrete solution be

\[ \bm{q}_N (\bm{x},t)^{(e)} = \sum_{k=1}^{P}\psi_k (\bm{x})\bm{q}_k^{(e)} \]

with \(P=p+1\) the number of nodes in the element \(e\). We use tensor-product bases \(\psi_{kji} = h_i(X_0)h_j(X_1)h_k(X_2)\).

To obtain a finite element discretization, we first multiply the strong form (3) by a test function \(\bm v \in H^1(\Omega)\) and integrate,

\[ \int_{\Omega} \bm v \cdot \left(\frac{\partial \bm{q}_N}{\partial t} + \nabla \cdot \bm{F}(\bm{q}_N) - \bm{S}(\bm{q}_N) \right) \,dV = 0 \, , \; \forall \bm v \in \mathcal{V}_p\,, \]

with \(\mathcal{V}_p = \{ \bm v(\bm x) \in H^{1}(\Omega_e) \,|\, \bm v(\bm x_e(\bm X)) \in P_p(\bm{I}), e=1,\ldots,N_e \}\) a mapped space of polynomials containing at least polynomials of degree \(p\) (with or without the higher mixed terms that appear in tensor product spaces).

Integrating by parts on the divergence term, we arrive at the weak form,

(5)¶\[ \begin{aligned} \int_{\Omega} \bm v \cdot \left( \frac{\partial \bm{q}_N}{\partial t} - \bm{S}(\bm{q}_N) \right) \,dV - \int_{\Omega} \nabla \bm v \!:\! \bm{F}(\bm{q}_N)\,dV & \\ + \int_{\partial \Omega} \bm v \cdot \bm{F}(\bm q_N) \cdot \widehat{\bm{n}} \,dS &= 0 \, , \; \forall \bm v \in \mathcal{V}_p \,, \end{aligned} \]

where \(\bm{F}(\bm q_N) \cdot \widehat{\bm{n}}\) is typically replaced with a boundary condition.

Note

The notation \(\nabla \bm v \!:\! \bm F\) represents contraction over both fields and spatial dimensions while a single dot represents contraction in just one, which should be clear from context, e.g., \(\bm v \cdot \bm S\) contracts over fields while \(\bm F \cdot \widehat{\bm n}\) contracts over spatial dimensions.

Time Discretization¶

For the time discretization, we use two types of time stepping schemes through PETSc.

Explicit Time-Stepping Method¶

The following explicit formulation is solved with the adaptive Runge-Kutta-Fehlberg (RKF4-5) method by default (any explicit time-stepping scheme available in PETSc can be chosen at runtime)

\[ \bm{q}_N^{n+1} = \bm{q}_N^n + \Delta t \sum_{i=1}^{s} b_i k_i \, , \]

where

\[ \begin{aligned} k_1 &= f(t^n, \bm{q}_N^n)\\ k_2 &= f(t^n + c_2 \Delta t, \bm{q}_N^n + \Delta t (a_{21} k_1))\\ k_3 &= f(t^n + c_3 \Delta t, \bm{q}_N^n + \Delta t (a_{31} k_1 + a_{32} k_2))\\ \vdots&\\ k_i &= f\left(t^n + c_i \Delta t, \bm{q}_N^n + \Delta t \sum_{j=1}^s a_{ij} k_j \right)\\ \end{aligned} \]

and with

\[ f(t^n, \bm{q}_N^n) = - [\nabla \cdot \bm{F}(\bm{q}_N)]^n + [S(\bm{q}_N)]^n \, . \]

Implicit Time-Stepping Method¶

This time stepping method which can be selected using the option -implicit is solved with Backward Differentiation Formula (BDF) method by default (similarly, any implicit time-stepping scheme available in PETSc can be chosen at runtime). The implicit formulation solves nonlinear systems for \(\bm q_N\):

(6)¶\[ \bm f(\bm q_N) \equiv \bm g(t^{n+1}, \bm{q}_N, \bm{\dot{q}}_N) = 0 \, , \]

where the time derivative \(\bm{\dot q}_N\) is defined by

\[ \bm{\dot{q}}_N(\bm q_N) = \alpha \bm q_N + \bm z_N \]

in terms of \(\bm z_N\) from prior state and \(\alpha > 0\), both of which depend on the specific time integration scheme (backward difference formulas, generalized alpha, implicit Runge-Kutta, etc.). Each nonlinear system (6) will correspond to a weak form, as explained below. In determining how difficult a given problem is to solve, we consider the Jacobian of (6),

\[ \frac{\partial \bm f}{\partial \bm q_N} = \frac{\partial \bm g}{\partial \bm q_N} + \alpha \frac{\partial \bm g}{\partial \bm{\dot q}_N}. \]

The scalar “shift” \(\alpha\) scales inversely with the time step \(\Delta t\), so small time steps result in the Jacobian being dominated by the second term, which is a sort of “mass matrix”, and typically well-conditioned independent of grid resolution with a simple preconditioner (such as Jacobi). In contrast, the first term dominates for large time steps, with a condition number that grows with the diameter of the domain and polynomial degree of the approximation space. Both terms are significant for time-accurate simulation and the setup costs of strong preconditioners must be balanced with the convergence rate of Krylov methods using weak preconditioners.

More details of PETSc’s time stepping solvers can be found in the TS User Guide.

Stabilization¶

We solve (5) using a Galerkin discretization (default) or a stabilized method, as is necessary for most real-world flows.

Galerkin methods produce oscillations for transport-dominated problems (any time the cell Péclet number is larger than 1), and those tend to blow up for nonlinear problems such as the Euler equations and (low-viscosity/poorly resolved) Navier-Stokes, in which case stabilization is necessary. Our formulation follows [HST10], which offers a comprehensive review of stabilization and shock-capturing methods for continuous finite element discretization of compressible flows.

SUPG (streamline-upwind/Petrov-Galerkin)

In this method, the weighted residual of the strong form (3) is added to the Galerkin formulation (5). The weak form for this method is given as

(7)¶\[ \begin{aligned} \int_{\Omega} \bm v \cdot \left( \frac{\partial \bm{q}_N}{\partial t} - \bm{S}(\bm{q}_N) \right) \,dV - \int_{\Omega} \nabla \bm v \!:\! \bm{F}(\bm{q}_N)\,dV & \\ + \int_{\partial \Omega} \bm v \cdot \bm{F}(\bm{q}_N) \cdot \widehat{\bm{n}} \,dS & \\ + \int_{\Omega} \nabla\bm v \tcolon\left(\frac{\partial \bm F_{\text{adv}}}{\partial \bm q}\right) \bm\tau \left( \frac{\partial \bm{q}_N}{\partial t} \, + \, \nabla \cdot \bm{F} \, (\bm{q}_N) - \bm{S}(\bm{q}_N) \right) \,dV &= 0 \, , \; \forall \bm v \in \mathcal{V}_p \end{aligned} \]

This stabilization technique can be selected using the option -stab supg.
SU (streamline-upwind)

This method is a simplified version of SUPG (7) which is developed for debugging/comparison purposes. The weak form for this method is

(8)¶\[ \begin{aligned} \int_{\Omega} \bm v \cdot \left( \frac{\partial \bm{q}_N}{\partial t} - \bm{S}(\bm{q}_N) \right) \,dV - \int_{\Omega} \nabla \bm v \!:\! \bm{F}(\bm{q}_N)\,dV & \\ + \int_{\partial \Omega} \bm v \cdot \bm{F}(\bm{q}_N) \cdot \widehat{\bm{n}} \,dS & \\ + \int_{\Omega} \nabla\bm v \tcolon\left(\frac{\partial \bm F_{\text{adv}}}{\partial \bm q}\right) \bm\tau \nabla \cdot \bm{F} \, (\bm{q}_N) \,dV & = 0 \, , \; \forall \bm v \in \mathcal{V}_p \end{aligned} \]

This stabilization technique can be selected using the option -stab su.

In both (8) and (7), \(\bm\tau \in \mathbb R^{5\times 5}\) (field indices) is an intrinsic time scale matrix. The SUPG technique and the operator \(\frac{\partial \bm F_{\text{adv}}}{\partial \bm q}\) (rather than its transpose) can be explained via an ansatz for subgrid state fluctuations \(\tilde{\bm q} = -\bm\tau \bm r\) where \(\bm r\) is a strong form residual. The forward variational form can be readily expressed by differentiating \(\bm F_{\text{adv}}\) of (4)

\[ \begin{aligned} \diff\bm F_{\text{adv}}(\diff\bm q; \bm q) &= \frac{\partial \bm F_{\text{adv}}}{\partial \bm q} \diff\bm q \\ &= \begin{pmatrix} \diff\bm U \\ (\diff\bm U \otimes \bm U + \bm U \otimes \diff\bm U)/\rho - (\bm U \otimes \bm U)/\rho^2 \diff\rho + \diff P \bm I_3 \\ (E + P)\diff\bm U/\rho + (\diff E + \diff P)\bm U/\rho - (E + P) \bm U/\rho^2 \diff\rho \end{pmatrix}, \end{aligned} \]

where \(\diff P\) is defined by differentiating (2).

Currently, this demo provides three types of problems/physical models that can be selected at run time via the option -problem. Advection-Diffusion, the problem of the transport of energy in a uniform vector velocity field, Isentropic Vortex, the exact solution to the Euler equations, and the so called Density Current problem.

Divergence of Diffusive Flux Projection¶

The strong residual in the SUPG operator in (7) and (8) features the term \(\nabla \cdot \bm F_{\text{diff}}(\bm{q}_N)\), the divergence of the diffusive flux. This term requires a second derivative to evaluate; first to evaluate \(\bm \sigma\) and \(\nabla T\) for \(F_{\text{diff}}\), the second for the divergence of the flux. For linear elements, the flux is constant within each element so the second derivative is zero, leading to accuracy issues. Additionally, libCEED does not currently support calculating double-derivatives. To circumvent these issues, we (optionally) perform a projection operation to get \(\nabla \cdot \bm F_{\text{diff}}(\bm{q}_N)\) at quadrature points. This was first proposed in [JCWS99]. There are two methods of achieving this implemented in HONEE, denoted as the direct and indirect methods.

Indirect Projection¶

Indirect projection is the method presented in [JCWS99]. Here, \(\bm F_{\text{diff}}\) is \(L^2\) projected onto the finite element space and then the divergence is taken from that FEM function. For linear basis functions, this leads to constant values of \(\nabla \cdot \bm F_{\text{diff}}\) within each element.

For compressible Navier-Stokes, this requires projecting 12 scalars-per-node: 4 conserved scalars (mass conservation does not have a diffusive flux term) in 3 dimensional directions. These 12 scalar finite element functions’ derivatives are then evaluated at quadrature points and the divergence is calculated. This method can be selected with -div_diff_flux_projection_method indirect.

Direct Projection¶

In the direct projection method, we perform an \(L^2\) projection of the divergence of the diffusive flux itself, \(\nabla \cdot \bm F_{\text{diff}}(\bm{q}_N)\). Then \(\nabla \cdot \bm F_{\text{diff}}\) itself is a function on the finite element space and can be interpolated onto quadrature points.

To do this, look at the RHS of the \(L^2\) projection of \(\nabla \cdot \bm F_{\text{diff}}(\bm{q}_N)\):

\[ \int_{\Omega} \bm v \cdot \nabla \cdot \bm F_{\text{diff}}(\bm{q}_N) \,dV \]

As noted, we can’t calculate \(\nabla \cdot \bm F_{\text{diff}}(\bm{q}_N)\) at quadrature points, so we apply integration-by-parts to achieve a calculable RHS:

\[ \int_{\partial \Omega} \bm v \cdot \bm{F}_{\text{diff}}(\bm q_N) \cdot \widehat{\bm{n}} \,dS - \int_{\Omega} \nabla \bm v \!:\! \bm{F}_{\text{diff}}(\bm{q}_N)\,dV \]

This form is what is used for calculating the RHS of the projection. After the projection, \(\nabla \cdot \bm F_{\text{diff}}(\bm{q}_N)\) is interpolated directly to quadrature points without any extra calculations necessary. For compressible Navier-Stokes, this means only projecting only 4 scalars-per-node.

The projection can be enabled using -div_diff_flux_projection_method direct.

General Information¶

The \(L^2\) projection in either method uses the standard mass matrix, which is rowsum lumped for performance by default. The linear solve for the projection can be controlled via -div_diff_flux_projection_ksp* flags.

Subgrid Stress Modeling¶

When a fluid simulation is under-resolved (the smallest length scale resolved by the grid is much larger than the smallest physical scale, the Kolmogorov length scale), this is mathematically interpreted as filtering the Navier-Stokes equations. This is known as large-eddy simulation (LES), as only the “large” scales of turbulence are resolved. This filtering operation results in an extra stress-like term, \(\bm{\tau}^r\), representing the effect of unresolved (or “subgrid” scale) structures in the flow. Denoting the filtering operation by \(\overline \cdot\), the LES governing equations are:

(15)¶\[ \frac{\partial \bm{\overline q}}{\partial t} + \nabla \cdot \bm{\overline F}(\bm{\overline q}) -S(\bm{\overline q}) = 0 \, , \]

where

(16)¶\[ \bm{\overline F}(\bm{\overline q}) = \bm{F} (\bm{\overline q}) + \begin{pmatrix} 0\\ \bm{\tau}^r \\ \bm{u} \cdot \bm{\tau}^r \end{pmatrix} \]

More details on deriving the above expression, filtering, and large eddy simulation can be found in [Pop00]. To close the problem, the subgrid stress must be defined. For implicit LES, the subgrid stress is set to zero and the numerical properties of the discretized system are assumed to account for the effect of subgrid scale structures on the filtered solution field. For explicit LES, it is defined by a subgrid stress model.

Table 1 SGS Model Options¶
Option	Description	Default value	Unit
`-sgs_model_type`	Type of subgrid stress model to use. Currently only `data_driven` is available	`none`	string

Data-Driven SGS Model¶

The data-driven SGS model implemented here uses a small neural network to compute the SGS term. The SGS tensor is calculated at nodes using an \(L^2\) projection of the velocity gradient and grid anisotropy tensor, and then interpolated onto quadrature points. More details regarding the theoretical background of the model can be found in [PJE22a] and [PJE22b].

The neural network itself consists of 1 hidden layer and 20 neurons, using Leaky ReLU as its activation function. The slope parameter for the Leaky ReLU function is set via -sgs_model_dd_leakyrelu_alpha. The outputs of the network are assumed to be normalized on a min-max scale, so they must be rescaled by the original min-max bounds. Parameters for the neural network are put into files in a directory found in -sgs_model_dd_parameter_dir. These files store the network weights (w1.dat and w2.dat), biases (b1.dat and b2.dat), and scaling parameters (OutScaling.dat). The first row of each files stores the number of columns and rows in each file. Note that the weight coefficients are assumed to be in column-major order. This is done to keep consistent with legacy file compatibility.

Note

The data-driven model parameters in the examples directory are not accurate and are for regression testing only.

Data-Driven Model Using External Libraries¶

There are two different modes for using the data-driven model: fused and sequential.

In fused mode, the input processing, model inference, and output handling were all done in a single CeedOperator. Fused mode is generally faster than the sequential mode, however fused mode requires that the model architecture be manually implemented into a libCEED QFunction. To use the fused mode, set -sgs_model_dd_implementation fused.

Sequential mode has separate function calls/CeedOperators for input creation, model inference, and output handling. By separating the three steps of the model evaluation, the sequential mode allows for functions calling external libraries to be used for the model inference step. The use of these external libraries allows us to leverage the flexibility of those external libraries in their model architectures.

PyTorch is currently the only external library implemented with the sequential mode. This is enabled with USE_TORCH=1 during the build process, which will use the PyTorch accessible from the build environment’s Python interpreter. To specify the path to the PyTorch model file, use -sgs_model_dd_torch_model_path. The hardware used to run the model inference is determined automatically from the libCEED backend chosen, but can be overridden with -sgs_model_dd_torch_model_device. Note that if you chose to run the inference on host while using a GPU libCEED backend (e.g. /gpu/cuda), then host-to-device transfers (and vice versa) will be done automatically.

The sequential mode is available using a libCEED based inference evaluation via -sgs_model_dd_implementation sequential_ceed, but it is only for verification purposes.

Table 2 Data-driven SGS Model Options¶
Option	Description	Default value	Unit
`-sgs_model_dd_leakyrelu_alpha`	Slope parameter for Leaky ReLU activation function. `0` corresponds to normal ReLU	0
`-sgs_model_dd_parameter_dir`	Path to directory with data-driven model parameters (weights, biases, etc.)	`./dd_sgs_parameters`	string
`-sgs_model_dd_model_implementation`	Which computational implementation to use for SGS DD model (`fused`, `sequential_ceed`, `sequential_torch`)	`fused`	string
`-sgs_model_dd_torch_model_path`	Path to the PyTorch `*.pt` file containing the DD inference model		string
`-sgs_model_dd_torch_model_device`	What hardware to perform the model inference on (`cpu`, `cuda`, `hip`, `xpu`)	Default matches the libCEED backend	string

Boundary Conditions¶

Synthetic Turbulence Generation (STG)¶

We use the STG method described in [SSST14]. Below follows a re-description of the formulation to match the present notation, and then a description of the implementation and usage.

Equation Formulation¶

\[ \bm{u}(\bm{x}, t) = \bm{\overline{u}}(\bm{x}) + \bm{C}(\bm{x}) \cdot \bm{v}' \]

\[ \begin{aligned} \bm{v}' &= 2 \sqrt{3/2} \sum^N_{n=1} \sqrt{q^n(\bm{x})} \bm{\sigma}^n \cos(\kappa^n \bm{d}^n \cdot \bm{\hat{x}}^n(\bm{x}, t) + \phi^n ) \\ \bm{\hat{x}}^n &= \left[(x - U_0 t)\max(2\kappa_{\min}/\kappa^n, 0.1) , y, z \right]^T \end{aligned} \]

Here, we define the number of wavemodes \(N\), set of random numbers \( \{\bm{\sigma}^n, \bm{d}^n, \phi^n\}_{n=1}^N\), the Cholesky decomposition of the Reynolds stress tensor \(\bm{C}\) (such that \(\bm{R} = \bm{CC}^T\) ), bulk velocity \(U_0\), wavemode amplitude \(q^n\), wavemode frequency \(\kappa^n\), and \(\kappa_{\min} = 0.5 \min_{\bm{x}} (\kappa_e)\).

\[ \kappa_e = \frac{2\pi}{\min(2d_w, 3.0 l_t)} \]

where \(l_t\) is the turbulence length scale, and \(d_w\) is the distance to the nearest wall.

The set of wavemode frequencies is defined by a geometric distribution:

\[ \kappa^n = \kappa_{\min} (1 + \alpha)^{n-1} \ , \quad \forall n=1, 2, ... , N \]

The wavemode amplitudes \(q^n\) are defined by a model energy spectrum \(E(\kappa)\):

\[ q^n = \frac{E(\kappa^n) \Delta \kappa^n}{\sum^N_{n=1} E(\kappa^n)\Delta \kappa^n} \ ,\quad \Delta \kappa^n = \kappa^n - \kappa^{n-1} \]

\[ E(\kappa) = \frac{(\kappa/\kappa_e)^4}{[1 + 2.4(\kappa/\kappa_e)^2]^{17/6}} f_\eta f_{\mathrm{cut}} \]

\[ f_\eta = \exp \left[-(12\kappa /\kappa_\eta)^2 \right], \quad f_\mathrm{cut} = \exp \left( - \left [ \frac{4\max(\kappa-0.9\kappa_\mathrm{cut}, 0)}{\kappa_\mathrm{cut}} \right]^3 \right) \]

\(\kappa_\eta\) represents turbulent dissipation frequency, and is given as \(2\pi (\nu^3/\varepsilon)^{-1/4}\) with \(\nu\) the kinematic viscosity and \(\varepsilon\) the turbulent dissipation. \(\kappa_\mathrm{cut}\) approximates the effective cutoff frequency of the mesh (viewing the mesh as a filter on solution over \(\Omega\)) and is given by:

\[ \kappa_\mathrm{cut} = \frac{2\pi}{ 2\min\{ [\max(h_y, h_z, 0.3h_{\max}) + 0.1 d_w], h_{\max} \} } \]

The enforcement of the boundary condition is identical to the blasius inflow; it weakly enforces velocity, with the option of weakly enforcing either density or temperature using the -weakT flag.

Initialization Data Flow¶

Data flow for initializing function (which creates the context data struct) is given below:

        flowchart LR
    subgraph STGInflow.dat
    y
    lt[l_t]
    eps
    Rij[R_ij]
    ubar
    end

    subgraph STGRand.dat
    rand[RN Set];
    end

    subgraph User Input
    u0[U0];
    end

    subgraph init[Create Context Function]
    ke[k_e]
    N;
    end
    lt --Calc-->ke --Calc-->kn
    y --Calc-->ke

    subgraph context[Context Data]
    yC[y]
    randC[RN Set]
    Cij[C_ij]
    u0 --Copy--> u0C[U0]
    kn[k^n];
    ubarC[ubar]
    ltC[l_t]
    epsC[eps]
    end
    ubar --Copy--> ubarC;
    y --Copy--> yC;
    lt --Copy--> ltC;
    eps --Copy--> epsC;

    rand --Copy--> randC;
    rand --> N --Calc--> kn;
    Rij --Calc--> Cij[C_ij]

This is done once at runtime. The spatially-varying terms are then evaluated at each quadrature point on-the-fly, either by interpolation (for \(l_t\), \(\varepsilon\), \(C_{ij}\), and \(\overline{\bm u}\)) or by calculation (for \(q^n\)).

The STGInflow.dat file is a table of values at given distances from the wall. These values are then interpolated to a physical location (node or quadrature point). It has the following format:

[Total number of locations] 14
[d_w] [u_1] [u_2] [u_3] [R_11] [R_22] [R_33] [R_12] [R_13] [R_23] [sclr_1] [sclr_2] [l_t] [eps]

where each [ ] item is a number in scientific notation (ie. 3.1415E0), and sclr_1 and sclr_2 are reserved for turbulence modeling variables. They are not used in this example.

The STGRand.dat file is the table of the random number set, \(\{\bm{\sigma}^n, \bm{d}^n, \phi^n\}_{n=1}^N\). It has the format:

[Number of wavemodes] 7
[d_1] [d_2] [d_3] [phi] [sigma_1] [sigma_2] [sigma_3]

The following table is presented to help clarify the dimensionality of the numerous terms in the STG formulation.

Math	Label	\(f(\bm{x})\)?	\(f(n)\)?
\( \{\bm{\sigma}^n, \bm{d}^n, \phi^n\}_{n=1}^N\)	RN Set	No	Yes
\(\bm{\overline{u}}\)	ubar	Yes	No
\(U_0\)	U0	No	No
\(l_t\)	l_t	Yes	No
\(\varepsilon\)	eps	Yes	No
\(\bm{R}\)	R_ij	Yes	No
\(\bm{C}\)	C_ij	Yes	No
\(q^n\)	q^n	Yes	Yes
\(\{\kappa^n\}_{n=1}^N\)	k^n	No	Yes
\(h_i\)	h_i	Yes	No
\(d_w\)	d_w	Yes	No

Runtime Options¶

To use the STG boundary condition, the -bc_inflow option should be set to the boundary faces that need the inflow (see Boundary conditions). The -stg_use flag is then used to enable/disable applying STG to those faces.

Table 3 STG Runtime Options¶
Option	Description	Default value	Unit
`-stg_use`	Enable STG for `bc_inflow` faces	`false`
`-stg_inflow_path`	Path to the STGInflow file	`./STGInflow.dat`
`-stg_rand_path`	Path to the STGRand file	`./STGRand.dat`
`-stg_alpha`	Growth rate of the wavemodes	`1.01`
`-stg_u0`	Convective velocity, \(U_0\)	`0.0`	`m/s`
`-stg_mean_only`	Only impose the mean velocity (no fluctutations)	`false`
`-stg_strong`	Strongly enforce the STG inflow boundary condition	`false`
`-stg_fluctuating_IC`	“Extrude” the fluctuations through the domain as an initial condition	`false`
`-stg_dx`	Set the element size in the x direction. Default is calculated for box meshes, assuming equispaced elements.		`m`
`-stg_h_scale_factor`	Scale element size for cutoff frequency calculation	\(1/p\)

Internal Damping Layer (IDL)¶

Note

IDL is not a boundary condition, but it’s primary application is for use with STG.

The STG inflow boundary condition creates large amplitude acoustic waves. We use an internal damping layer (IDL) to damp them out without disrupting the synthetic structures developing into natural turbulent structures. This implementation was inspired by [SSST14], but is implemented here as a ramped volumetric forcing term, similar to a sponge layer (see 8.4.2.4 in [Col23] for example). It takes the following form:

\[ S(\bm{q}) = -\sigma(\bm{x})\left.\frac{\partial \bm{q}}{\partial \bm{Y}}\right\rvert_{\bm{q}} \bm{Y}' \]

where \(\bm{Y}' = [P - P_\mathrm{ref}, \bm{0}, 0]^T\), and \(\sigma(\bm{x})\) is a linear ramp starting at -idl_start with length -idl_length and an amplitude of inverse -idl_decay_rate. The damping is defined in terms of a pressure-primitive anomaly \(\bm Y'\) converted to conservative source using \(\partial \bm{q}/\partial \bm{Y}\rvert_{\bm{q}}\), which is linearized about the current flow state. \(P_\mathrm{ref}\) has a default value equal to -reference_pressure flag, with an optional flag -idl_pressure to set it to a different value.

Table 4 IDL Runtime Options¶
Option	Description	Default value	Unit
`-idl_decay_time`	Characteristic timescale of the pressure deviance decay. The timestep is good starting point	`-1` (disabled)	`s`
`-idl_start`	Start of IDL in the x direction	`0`	`m`
`-idl_length`	Length of IDL in the positive x direction	`0`	`m`
`-idl_pressure`	Pressure used for IDL reference pressure	`-reference_pressure`	`Pa`

Theory and Background¶

The Navier-Stokes Equations¶

Finite Element Formulation (Spatial Discretization)¶

Time Discretization¶

Explicit Time-Stepping Method¶

Implicit Time-Stepping Method¶

Stabilization¶

Navier-Stokes \(\tau\) definition

Advection-Diffusion \(\tau\) definition

Euler \(\tau\) definition

Divergence of Diffusive Flux Projection¶

Indirect Projection¶

Direct Projection¶

General Information¶

Subgrid Stress Modeling¶

Data-Driven SGS Model¶

Data-Driven Model Using External Libraries¶

Boundary Conditions¶

Synthetic Turbulence Generation (STG)¶

Equation Formulation¶

Initialization Data Flow¶

Runtime Options¶

Internal Damping Layer (IDL)¶